文件名称:mization
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Recently more research works are focused on multi-objective particle swarm optimization
algorithm (MOPSO) due to its ability of global and local search for solving multi-objective
optimization problems (MOOPs) however, most of existing MOPSOs cannot achieve satisfactory
results in solution quality. This paper proposes an efficient hybrid multi-objective
particle swarm optimization with a multi-objective dichotomy line search (MOLS), named
MOLS-MOPSO, to deal with such problem. MOLS-MOPSO combines an effective particle
updating strategy with the local search of MOLS. The effective particle updating strategy
is used for global search to deal with premature convergence and diversity maintenance
within the swarm-Recently more research works are focused on multi-objective particle swarm optimization
algorithm (MOPSO) due to its ability of global and local search for solving multi-objective
optimization problems (MOOPs) however, most of existing MOPSOs cannot achieve satisfactory
results in solution quality. This paper proposes an efficient hybrid multi-objective
particle swarm optimization with a multi-objective dichotomy line search (MOLS), named
MOLS-MOPSO, to deal with such problem. MOLS-MOPSO combines an effective particle
updating strategy with the local search of MOLS. The effective particle updating strategy
is used for global search to deal with premature convergence and diversity maintenance
within the swarm
algorithm (MOPSO) due to its ability of global and local search for solving multi-objective
optimization problems (MOOPs) however, most of existing MOPSOs cannot achieve satisfactory
results in solution quality. This paper proposes an efficient hybrid multi-objective
particle swarm optimization with a multi-objective dichotomy line search (MOLS), named
MOLS-MOPSO, to deal with such problem. MOLS-MOPSO combines an effective particle
updating strategy with the local search of MOLS. The effective particle updating strategy
is used for global search to deal with premature convergence and diversity maintenance
within the swarm-Recently more research works are focused on multi-objective particle swarm optimization
algorithm (MOPSO) due to its ability of global and local search for solving multi-objective
optimization problems (MOOPs) however, most of existing MOPSOs cannot achieve satisfactory
results in solution quality. This paper proposes an efficient hybrid multi-objective
particle swarm optimization with a multi-objective dichotomy line search (MOLS), named
MOLS-MOPSO, to deal with such problem. MOLS-MOPSO combines an effective particle
updating strategy with the local search of MOLS. The effective particle updating strategy
is used for global search to deal with premature convergence and diversity maintenance
within the swarm
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1-s2.0-S0377042714005354-main.pdf
1-s2.0-S0925231215002210-main.pdf